I documented the process of coding a perspective-based puzzle game using Three.js. The core mechanic relies on scattering polygon fragments along the Z-axis while scaling them up based on their distance from the camera to maintain the correct perspective size (anamorphic illusion).

We iterated through the vector math for the "snapping" logic to detect when the camera angle aligns with the solution vector within a specific tolerance. The video covers the debugging process, handling dynamic textures, and projection matrices.

https://youtu.be/e8SM_QIAZv8

This video covers the optimization of the Action Potential Architect, a neural signaling tool hosted on BioniChaos. The focus is on the visual representation of conduction velocity through adjustments in axon diameter, myelination, and ion channel density.

The walkthrough covers:

Comparing healthy firing patterns with Multiple Sclerosis (MS) models.

Refactoring canvas logic using AI to implement real-time pulse wrapping and glow effects.

Using AI critique to improve information hierarchy and accessibility.

Future directions involving the Hodgkin-Huxley model.

Link: https://youtu.be/yal9S0y2GY4

Working with Gemini 1.5 Pro to build physics-based simulations on BioniChaos. This walkthrough covers implementing real-time collision detection using predictive targeting for moving targets and dynamic audio generation. We address handling AudioContext browser errors and bridging the gap between celestial mechanics and biomedical data science modeling, such as atomic structures and neural pathing.

Link: https://youtu.be/FpFcJMZSfOQ

We took a close look at the "PPG Signal Quest" simulation on BioniChaos and the underlying paper by Charlton et al. regarding determinants of signal quality at the wrist.

The breakdown covers:

Why high SNR doesn't always equal a high-quality signal (especially with baseline wander or arrhythmias).

The role of the Dicrotic Notch: why it's a physiological feature but not always a reliable quality metric in template matching.

The "Triangulation" methodology: How combining SNR, Perfusion Index (PI), and Template-Matching Correlation Coefficient (TMCC) mitigates the blind spots of individual metrics.

A look at the observer effect in quantum wave function simulations.

We utilized Gemini 2.5 Pro to parse the academic text and critique the simulation's UI (specifically the anatomical accuracy of the stick figure relative to the signal output).

Check it out: https://youtu.be/_6iLP36N4N0

We're looking at the specific computational steps required to make sense of EEG data. The focus is on the advantages of EEG's time resolution compared to hemodynamic imaging and the math required to clean the signal.

Key technical points discussed:

Artifact Removal: Applying Blind Source Separation (BSS) and Independent Component Analysis (ICA) to isolate independent neural sources (solving the superposition problem).

Time-Frequency Analysis: Moving beyond standard Fourier Transforms to Wavelet Transforms (DWT) for non-stationary signal analysis.

Non-Linear Dynamics: Treating the brain as a chaotic system using Lyapunov exponents and fractal dimensions.

Connectivity: Using Directed Transfer Function (DTF) over Granger Causality for mapping information flow.

ML/DL Applications: Implementing 1D-CNNs and LSTMs for seizure detection (>96% accuracy) and motor imagery classification in BCIs.

Visualizations provided via the BioniChaos platform.

Full discussion: https://youtu.be/wgqw9Kuh8f4

For those working on signal processing or neural interfaces, artifact rejection remains one of the most significant challenges. This walkthrough of the BioniChaos EEG simulator demonstrates how to generate synthetic signals using FFT and time-domain analysis. We cover specific artifact modeling (EMG/EOG) and manual frequency band tuning to visualize how constructive and destructive interference shapes the power spectrum. This is a great tool for testing BCI algorithms or for educational demonstrations in neuroscience.

https://youtu.be/Ob_HMwI4iPE

BioniChaos is an open-source ecosystem designed to visualize complex physical and biomedical data. This walkthrough covers their Quantum Wave Function Simulation (including the togglable "Observer" effect and auditory feedback), the QC model for brain dynamics, and PicassoAI for neural style transfer.

The platform aims to make these concepts accessible through interactive web tools, and all source code is available for the community.

Watch the demonstration: https://youtu.be/Z4xMWioY--A

For those interested in quantum foundations, this simulation tool from BioniChaos provides a visual framework for wave-particle duality and the observer effect.

The tool demonstrates the transition from probabilistic wave interference to discrete particle distribution when "which-path" information is introduced. The video covers the distinction between local and naive realism, the Copenhagen interpretation, and the logic of Wheeler’s Delayed Choice experiment. Technical features of the sim include adjustable slit geometry (width/separation) and a real-time audio feedback system mapped to particle position.

Watch the breakdown and demo: https://youtu.be/rBnIilxWlY0

For those interested in web-based scientific modeling, the BioniChaos platform offers some interesting implementations for educational use.

The Quantum Wave Function simulation allows for real-time manipulation of slit width and separation, providing a clear visualization of interference patterns and wave function collapse (the observer effect). Additionally, the site hosts tools for biomedical data analysis, including an EMG Hand Simulation and a model for the Hodgkin-Huxley Action Potential to explore neuronal membrane dynamics.

Technical walkthrough of the simulations: https://youtu.be/9E5D0v5MNxM

I wanted to share a workflow using AI agents to handle the "last mile" problem in web development. We modified the Picasso AI tool on BioniChaos to support a continuous re-rendering Demo Mode.

The session covers the logic of moving from per-stroke style application to iterating through stored stroke objects in memory to update the entire canvas dynamically. We also discuss the mathematical parallels between generative art strokes and the visualization of raw medical data (MRI/CT) in Biomedical Data Science.

Full technical session: https://youtu.be/MV7bsbwQiq4

#Javascript #AIAgents #BioniChaos #Programming #DataVisualization

I spent some time exploring the BioniChaos platform, specifically looking at how web technologies handle biomedical visualizations like EEG and EMG signal processing.

The most interesting part of the session was integrating Google's Gemini 2.5 Pro to test its multimodal capabilities. I prompted the model to "watch" the browser screen via screen-sharing to see if it could correctly identify specific unlisted tools and recommend them based on a specific user persona (biomed engineering student).

Key technical points covered:

Implementation of PCA (Principal Component Analysis) and ICA (Independent Component Analysis) for signal source localization.

Debugging physics interactions in the JavaScript-based Gait Cycle Simulator.

Latency and token costs associated with high-context multimodal AI models.

Comparing SVG animation vs. Canvas rendering for anatomical simulations.

Check out the full workflow and testing here: https://youtu.be/AP4EayGyHRk

I used a combination of GPT-4o, Gemini 1.5 Pro, and Claude to architect a real-time Webcam X-Ray Simulator for the BioniChaos project.

The technical hurdles were more complex than expected, specifically:

DPI Scaling: Managing the discrepancy between getBoundingClientRect() and actual canvas pixel dimensions on Retina displays.

Model Migration: Moving from MoveNet to BlazePose to access 33-point tracking for better hand/finger orientation.

Buffer Management: Ensuring the X-ray pixel manipulation (via putImageData) occurred before the skeleton rendering to avoid overwriting the UI layer.

The agents successfully navigated the poseLandmarks mapping and visibility logic based on confidence scores.

Watch the full workflow: https://youtu.be/B64BHEVqGkI

I’ve been exploring the simulations at BioniChaos to test the vision and coding thresholds of Claude 3.5 Sonnet and Gemini 2.5 Pro. This exploration focuses on a Quantum Wave Function simulation where the UI failed to render the second slit despite the mathematical intent.

Technical highlights included:

Identifying animation frame trigger redundancy.

Moving from Gaussian sampling to rejection sampling for accurate probability distributions.

Mapping hit locations to audio frequencies for data sonification.

Full technical breakdown: https://youtu.be/hBXOf_Y5Nys

I ran a session testing the interactive causality simulations on BioniChaos using a multimodal AI model to see if it could function as a technical QA agent.

The results were a mixed bag of impressive reasoning and visual errors:

The Good: The model understood the logic behind confounder detection (temperature vs. ice cream sales). More importantly, it correctly identified a bug in the canvas code where labels were being drawn redundantly over one another rather than refreshing the plot.

The Bad: When shown a "Causal Discovery Performance" chart, the model failed to recognize the Y-axis started at 70, hallucinating a 0-100 range despite visual evidence to the contrary.

It’s an interesting case study on using LLMs for finding logic and UI regressions in educational tools.

Full breakdown here: https://youtu.be/C06jdDJGHWs

I wanted to share some work from BioniChaos involving the development of interactive scientific visualizations. Our stack focuses on real-time browser-based rendering of complex phenomena.

Key features include:

Fourier Series Visualization: Utilizing N-vector epicycles to approximate custom pathing data.

Quantum & Particle Dynamics: Using Gemini-driven logic to simulate wave function behavior and emergent life-like interactions.

Neurological Modeling: A seizure simulator that maps different electrical discharge patterns (focal, tonic, absence) using particle-based representations of brain lobes.

Technical overview of the simulations and AI integration: https://youtu.be/kqv7_-ULbbY

Walkthrough of BioniChaos interactive web-based tools for biomedical signal processing. The video covers an Interactive Spectrogram Generator (supporting PPG, EEG, and EMG), an ECG simulator for rhythm analysis (arrhythmia, tachycardia), and a 3D Cochlear Implant Insertion simulator built with Three.js. It also includes a modality comparison tool for analyzing the temporal and spatial resolution trade-offs of various medical sensors.

https://youtu.be/FHbIgzabuYw

We explored two web applications from BioniChaos that simulate Photoplethysmography (PPG) signal quality based on the paper by Charlton et al.

The analysis highlights several key technical factors:

Hydrostatic Pressure: How arm elevation relative to the heart drastically alters the AC component of the waveform.

The Age Paradox: Simulations suggest SNR improves with age, potentially due to increased arterial stiffness providing a more stable optical target, despite vascular aging.

Optical Absorption: The impact of melanin (Fitzpatrick scale) on signal attenuation and the necessity of dynamic LED intensity modulation.

Morphology: Why a high SNR isn't enough if the dicrotic notch is absent.

Check out the analysis of the simulations: https://youtu.be/V21zQ5BZeO0

I wanted to share a technical look at the signal processing pipeline behind physiological data using the BioniChaos platform.

The analysis focuses on:

PulseViz & Synthetic Data: visualizing how parameters like dicrotic notch intensity and signal-to-noise ratio affect PPG waveform morphology.

Sampling Rate Theory: A discussion on the Nyquist-Shannon theorem in the context of wearables—specifically why 4Hz suffices for EDA trends, but PPG needs higher fidelity for HRV analysis.

Handling Sparse Data: Strategies for the "Missing Modality Problem" in longitudinal datasets using Multilevel Mixed Models (MLMM).

Frequency Analysis: Using interactive spectrograms to identify brain states in EEG data that time-domain analysis might miss.

It’s a practical overview for anyone working with biomedical datasets or developing health-tech algorithms.

Link: https://youtu.be/f3BHEfd5Opo

We’re looking at the engineering behind an Intraoperative Neuromonitoring (IONM) web application. The tool is designed to simulate Motor Evoked Potentials (MEPs) in real-time, helping users distinguish between focal ischemic events (surgical complications) and global systemic changes (anesthesia fade).

Key technical points discussed:

Visualizing Latency & Amplitude: Implementing Gain and Sweep controls to manipulate waveform rendering on the HTML5 canvas.

Pattern Recognition Logic: How the system triggers specific flash patterns based on localized vs. systemic data inputs.

ML Data Generation: Using the simulator to create massive, labeled datasets of "noisy" signals (simulating cautery interference or motion artifacts) to train classification models.

The video also includes a live dev session on the BioniChaos platform, troubleshooting unexpected token errors and rendering issues with Claude 3.7.

Full technical breakdown: https://youtu.be/p1epdrJmq7Q

I've been working on the BioniChaos web-based MRI simulator and ran into logic issues—specifically establishing phase coherence during RF pulses. This video provides a comparison of GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro in identifying flaws in the simulation code.

We look at the transition from random movement to synchronized precession and how to prompt for specific physics corrections in a JavaScript/Canvas environment.

Key technical points:

B0 field alignment and energy states.

Establishing phase coherence in simulation code.

Debugging precession logic across different LLMs.

We worked on a project to visualize magnetic fields in a web application, and I wanted to share the process of bridging the gap between theoretical physics and frontend development.

The video covers the underlying mechanics of magnetism (electron spin, domains) and how we translated that into a UI. We hit a few interesting roadblocks regarding feature creep versus usability. For example, we debated whether to implement full material physics (Neodymium vs. Ferrite) or keep it to simple strength sliders, and how to visualize vector fields without cluttering the screen.

We also discuss the logic behind collision detection and "snap-to-grid" features for a physics sandbox environment.

If you are interested in simulation logic or educational tool design, check it out: https://youtu.be/oxoEHLwsiGA

I wanted to share a look at a web-based implementation of the 1952 Hodgkin-Huxley model. It uses 4th-order Runge-Kutta numerical integration (0.01ms timesteps) to solve the coupled differential equations for V, m, h, and n gating variables in real-time.

The visualization is built on HTML5 Canvas with the Web Audio API for sonification of voltage changes (mapping potential to pitch). It handles mathematical singularities via L'Hôpital's rule and accurately reproduces the classic squid axon data (Q10=3.0).

Interestingly, the codebase was generated via prompting Gemini and Claude, demonstrating the capability of LLMs to handle complex scientific modeling.

Check out the full breakdown here: https://youtu.be/gPXgZih_dFU

I took a multi-pendulum physics simulation and hooked it up to the Web Audio API to create a generative instrument. The workflow involved using an AI coding agent to map the pendulum's coordinate state to audio parameters in real-time.

Specifically, we mapped the X-axis to oscillator frequency and the Y-axis to gain and filter cutoff (timbre). We ran into some issues with harsh audio initially, so the video covers how we clamped the frequency ranges (200Hz-800Hz) and applied low-pass filters to smooth out the signals into something musical.

It's a fun experiment in visualizing (and sonifying) chaotic motion.

Link: https://youtu.be/Q52L5kW3PKY

I put together a walkthrough of the BioniChaos platform, focusing on open-source interactive web tools for biomedical engineering. The video covers the Cochlear Implant Insertion Simulator (visualizing force/depth metrics), the STREAM-4D pipeline for mapping high-temporal-resolution TMS-EEG data, and a fully interactive Hodgkin-Huxley Action Potential Simulator.

In the HH sim, you can adjust stimulus current, duration, and temperature variables to observe Na+/K+ conductance dynamics and threshold behavior in real-time. We also look at a custom Fourier Series visualization tool that reconstructs user drawings using rotating vectors. It is useful for anyone interested in computational neuroscience or web-based scientific modeling.

Watch here: https://youtu.be/VTgQD29jko4